Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

被引：88

作者：

Angelis, Dimitrios ^{[1
]}

Sofos, Filippos ^{[1
]}

Karakasidis, Theodoros E. E. ^{[1
]}

机构：

[1] Univ Thessaly, Dept Phys, Condensed Matter Phys Lab, Lamia 35100, Greece

来源：

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING | 2023年 / 30卷 / 06期

关键词：

MACHINE LEARNING-MODELS; GLOBAL SOLAR-RADIATION; MATERIALS DISCOVERY; ENERGY-CONSUMPTION; NEURAL-NETWORKS; DESIGN; PERFORMANCE; PREDICTION; OPPORTUNITIES; CHALLENGES;

D O I：

10.1007/s11831-023-09922-z

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.

引用

页码：3845 / 3865

页数：21

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